Water Saturation Calculation Research Articles

Exploring comparative heterogeneity management for precise water saturation assessment in carbonate formations: Dean-Stark measurements and beyond

Accurate determination of water saturation is a critical aspect of reservoir characterization and development potential of a hydrocarbon field. The inherent heterogeneity in carbonate reservoirs significantly influences the Archie equation coefficients and, consequently, water saturation calculations. In this study, 160 direct core sample water saturation measurements using the Dean-Stark method have been used. The primary goal was to identify the most suitable reservoir heterogeneity management approach for water saturation calculation. The research focuses on the Dalan and Kangan formations (equivalent to Khuff Formation), known as some of the most hydrocarbon-rich (gas) reservoirs globally, located in the western part of the Persian Gulf. To manage reservoir heterogeneity, we employed various techniques, including Winland R35, flow zone indicator, Lucia, pore types, and electrofacies, utilizing porosity and permeability core data, thin section studies, and well-logging data to classify the studied intervals into more homogeneous categories. Subsequently, we measured Archie parameters and, for the first time, water saturations derived from Dean-Stark measurements were compared with Archie saturations from various heterogeneity management methods. Additionally, the influence of geological and petrophysical parameters on the accuracy of water saturation calculations in different rock types is discussed. Our results highlighted the significance of geometrical attributes of pore types, pore throat radius, and permeability as key factors affecting the precision of water saturation calculations. Moreover, our investigation revealed that Lucia's rock typing methodology exerted a substantial influence on the control of permeability and the distribution of water saturation within the reservoir. Our analysis revealed that the electrofacies method provided the most precise estimates for water saturation, with a mean difference of 0.07 (v/v) units compared to the Dean-Stark method. Subsequently, the accuracy of predicted water saturation in rock types determined by the Lucia method, with a mean error of 0.09 (v/v), ranked second among the rock typing methods. Winland R35 and flow zone indicator methods exhibited the highest uncertainty in water saturation estimation, with mean differences of 0.23 and 0.21 (v/v), respectively, compared to the Dean-Stark method.

Chemical elements migration in underground waters of mining territory

Relevance. The need to study the forms of migration of chemical elements in the groundwater of flooded copper pyrite mines in order to correctly understand and predict their transfer and distribution in hydrogeochemical fields. Ground and surface waters are a complex mixture of substances in which, depending on pH, Eh and t °С, phase transitions are formed with subsequent dissolution or precipitation of minerals. To solve these problems, numerical hydrogeomigration modeling is used, including physicochemical, which allows drawing conclusions about the scale of pollution and the localization of such areas for subsequent development of measures to improve the state of the hydrosphere. Aim. To determine the forms of metal migration in groundwater and to calculate water saturation indices in relation to minerals. Objects. Groundwater in the territory of the closed Levikha copper pyrite mine. Methods. Laboratory studies of groundwater were carried out using flame emission spectrometry, flame atomic absorption, photometric method with Nessler's reagent, titrimetric, mercumetric and potentiometric methods; mass spectrometry with ionization in inductively coupled plasma and gravimetric method. Physical and chemical modeling using the software product Visual MINTEQ 3.1. Results. According to the results of processing chemical analyzes and physical and chemical modeling, all tested wells are divided into groups: 1) wells no. 1, 2, 3 and 6, located within the former mining allotment (near the Levikha XIV mine, a man-made reservoir, the Levikha II mine and a neutralization station); 2) represented by water in wells no. 4 near the shaft of the mine Tsentralnaya and no. 5 near the dump Yuzhny; 3) well no. 7, located near the mouth of the Levikha river. Saturation indices and forms of migration of components in the aquatic environment make it possible to identify the scale of pollution and the localization of such areas. Thus, with the current pumping system, there is no large-scale pollution of groundwater at the Levikha mine. It is localized in the area of the Tsentralny mine shaft (well no. 4) and the Yuzhny dump (well no. 5).

Prediction of saturation exponent for subsurface oil and gas reservoirs using soft computing methods

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous.

Influences of Geological and Petrophysical Attributes on Electrical Resistivity–Based Reserve Evaluation: Enhancing Carbonate Reservoir Classification, Permian-Triassic Reservoirs of Southern Iran

Summary The strong heterogeneity of carbonate reservoirs makes it challenging to assess the spatial distribution of fluid behavior, geological attributes, petrophysical properties, and estimate oil reserves. The diversity in facies and diagenetic processes contributes significantly to the heterogeneity in these reservoirs. The ability to accurately characterize and manage hydrocarbon reservoirs heterogeneity hinges on a comprehensive understanding of geological and petrophysical attributes such as water saturation, porosity, permeability, and electrical conductivity. In the quest for optimizing hydrocarbon reservoir identification and management, understanding the intricate relationship between the geological, petrophysical characteristics, and electrical conductivity of reservoir rocks is paramount. Electrical conductivity serves as an indicator of the structural attributes of pore networks, reflecting sedimentary and diagenetic influences on reservoir quality. Despite the extensive use of electrical resistivity in hydrocarbon reservoir assessments, accurate interpretation of its variations remains a significant challenge, which is due to complex geological factors. Thus, acquiring a thorough insight into the impact of geological and petrophysical attributes on electrical resistivity is crucial for a dependable assessment of hydrocarbon reservoirs. In this article, we explore the complex interplay between various geological and petrophysical factors and their impact on the electrical conductivity of rocks, which serves as a vital parameter in the assessment of hydrocarbon reservoirs. The focus is on how sediment texture, pore types, depositional environments, diagenetic processes, and characteristics of pore throat radius influence the electrical properties of rocks. The objective of this comprehensive approach is to decipher the geological and petrophysical modifications using electrical data, thereby improving the analysis of electrical discrepancies. Moreover, the accuracy of categorizing rocks based on their electrical characteristics is evaluated to effectively manage reservoir heterogeneity. Different data were gathered from an exploratory well situated in the western Persian Gulf. This data set comprised evaluations of 1,370 thin sections, 1,110 porosity and permeability data, 32 scanning electron microscopy (SEM) analyses, wireline logs, 29 mercury injection capillary pressure (MICP) data, 58 formation resistivity factors (FRFs), 20 formation resistivity indexes (FRIs), and 157 Dean-Stark measurements. Rocks were classified into different groups with similar electrical behavior utilizing the electrical quality index (EQI) approach. The efficiency of this method in managing reservoir heterogeneity, as one of the techniques for determining electrical rock type, was assessed, particularly by comparing the precision of predicted water saturation with Dean-Stark saturation data. The findings of this study demonstrated that diagenetic processes, especially dolomitization and dissolution, have the most significant impact on variations in the electrical conductivity of rocks. These processes govern pore size, distribution, pore types, and the radii of pore throats. According to the findings of this research, categorizing rocks based on their electrical characteristics improves the precision of water saturation predictions. In additionally, the use of constant Archie coefficients in water saturation calculations results in an overestimation of water saturation, leading to an underestimation of hydrocarbon reserves in the reservoir. The results of this research allow for more knowledgeable decision-making concerning reservoir efficiency, hydrocarbon reserves, production techniques, and increased oil recovery.

Calculation and Comparison of Water Saturation in Carbonate Formation by Different Methods

Water saturation is an important governing quality and quantity of the hydrocarbon evaluation, as reserve estimation. The challenge of the reservoir engineer is to resolve and understand the differences distribution of water saturation content by using different techniques. However, though out this work illustrate variety models to calculate water saturation which is compared with core measured. Therefore, water saturation is calculated using different models; Archie, Simandoux, Indonesia which are compared with Leverett J-Function (capillary pressure measurements). Archie's parameters of the studied reservoir rock; Cementation Factor (m) and Saturation Exponent (n) are derived from the log data used. Error of the water saturation estimation may significantly impose bias in the estimation of both quantities. In spite of, there are various factors affecting potential error in water saturation estimation, such as, average permeability, average porosity and Archie's parameters are used. Consequently, the comparison between results indicates that, Indonesia and Simandoux models are show the smallest average absolute error values, where Archie's model common one used for water saturation in Carbonate rocks. Three wells are selected to present his comparison.

A novel model for oil reservoirs considering stress sensitivity and dynamic relative permeability

ABSTRACT Stress sensitivity and dynamic relative permeability are undeniable phenomenon in oil reservoir numerical simulation. In this paper, a complex two-phase flow model was established considering the deformation of porous media and relative permeability dynamic changes during water injection. A sensitivity analysis on this new model is carried out. The results reveal that stress sensitivity is a key factor that cannot be ignored when using reservoir numerical simulation to calculate water saturation. When the stress-sensitivity coefficient is not considered, the average water saturation of the model is 51.59%. When the stress-sensitivity coefficient is 0.12MPa−1, the average water saturation of the model is 48.45%. After considering stress sensitivity, the average water saturation of the model decreases by 6.1%. The water saturation difference between the new model and the traditional model is mainly reflected in the vicinity of the water injection well. Additionally, the influence of the range of changes in reservoir permeability on the calculation of water saturation by the new model cannot be ignored.

Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy

In a mature middle and lower Gharif field in Oman, uncertainties surrounding initial water saturation and early water breakthroughs of unknown sources and paths suggest the presence of significant bypassed oil. In order to determine the areas with remaining oil, petrophysical and logging data of seven wells were processed using Techlog software and imported into Petrel software for modelling and simulation. Porosity was calculated using the Electric Propagation Time log and was utilized to evaluate the presence of oil, particularly in the upper tight zone of the formation. Despite the low resistivity readings in the highly porous layers, caused by good network connectivity and high formation water salinity, the resistivity contrast was sufficient to differentiate them from the oil zone. However, the calculated water saturation (Sw) in the tight top oil zone was high, consistent with the observed water production in the field. To improve the match between production data and simulation results, sensitivity analyses were conducted on various permeability anisotropy and relative permeability values within the model. The analyses showed that core-derived permeability anisotropy (vertical to horizontal ratio of 1:1) yielded a better history match for water production compared to the conventionally used value of 1:10. Water saturation maps were generated at the start and the end of production to highlight saturation distribution within the reservoirs. The maps revealed that in the lower porous part, the oil was fully depleted around the wells but remained trapped in the undrilled areas.

Logging-While-Drilling Laterolog vs. Electromagnetic Propagation Measurements: Which Is Telling the True Resistivity?

Summary The electromagnetic propagation (EMP) measurement frequently acquired with logging-while-drilling (LWD) tools in high-angle wells is sensitive to geometrical effects that can mask the true formation resistivity. Less commonly used, the LWD laterolog measurement is sometimes perceived as providing data too shallow to give a true formation resistivity (Rt). In this paper, we presents modeling and actual examples to demonstrate that the laterolog can often provide a superior resistivity measurement for formation evaluation to that of the LWD EMP tool. We examine the laterolog and EMP resistivities in several high-angle wells crossing carbonate formations in 8.5-in. and 6.125-in. hole sizes. In the 8.5-in. sections, producers and water injectors (high- and low-resistivity ranges) were evaluated. In the 6.125-in. sections, one reservoir sandwiched between two very high-resistivity layers and another borehole in a highly fractured reservoir were examined. The laterolog data were corrected for invasion using a 1D inversion of the memory data. Structure-based forward modeling was used to examine and explain the differences between the laterolog and EMP resistivity measurements. In the first example in a thick low-resistivity water reservoir, laterolog resistivity and EMP resistivity agree, showing that the two tools provide the same measurement when no geometrical effects are present. In the first part of the second example, a reservoir zone was initially drilled only with the LWD EMP resistivity measurement. The LWD laterolog was run several days later, and the resistivity data read much lower in the relogged section compared with the EMP resistivity. The laterolog 1D inversion was unable to resolve Rt because of the excessively deep invasion that occurred over the course of several days. In the second part of the second example, the laterolog resistivity showed a clear conductive invasion profile. While the deepest laterolog real-time resistivity data indicated lower resistivity than the EMP resistivity, the true resistivity, Rt (invasion-corrected 1D-inverted laterolog resistivity), matched the EMP Rt resistivity. This result validated both measurements and emphasized that the differences were due to invasion. The first two examples demonstrated that when acquired in normal drilling conditions (within 1–2 hours of drilling the section), the laterolog measurements can provide uninvaded formation resistivity even in the presence of invasion. A reservoir in another example was sandwiched between resistive layers that caused difficult-to-explain elevated EMP resistivity readings. Structural modeling reproduced the elevated behavior of the EMP data and explained the differences between resistivity measurements. This result showed that the laterolog is better suited to evaluate resistivity in thin reservoirs where there is a high-resistivity contrast to the adjacent layer. Finally, fractured reservoir examples are presented, which show that both the laterolog and EMP can be affected by the presence of fracture swarms. The examples presented in this paper demonstrate that in high-angle wells, when acquired under normal drilling conditions, invasion-corrected laterolog resistivity is often nearer to Rt than EMP resistivity. In those cases, the laterolog measurement provides data that are better inputs to water saturation calculations. Multiple examples from high-angle wells illustrate the findings.

Petrophysics of Low Resistivity Reservoir

Low resistivity reservoirs have long been known in the oil and gas industry as reservoirs that have significant potential and contribution to oil and gas production. This reservoir is very unique because of its relatively low resistivity readings (< 5 Ohm) and often other log responses do not show the way carbonate-containing reservoirs do, so they are often overlooked and not considered as potential carbon reservoirs. The low resistivity reading also causes the Water Saturation (Sw) value to be too high, which contributes to why the reservoir is overlooked. Theoretically, the factors that cause low resistivity readings are high clay/shale content, fine grain size, high salinity air formation, presence of microporosity and presence of conductive minerals which affect resistivity log readings in addition to other things such as low structured reservoirs. and there is a significant slope of the layers. Based on statistic reports, generally in low resistivity reservoirs there is a combination of two or more of the causal factors mentioned. This study aims to identify the characteristics of low resistivity reservoirs in the Upper Cibulakan Formation, West Java Basin, especially from log readings and recognition of what factors support reservoir formation both from the results of core and log analysis as well as selecting combinations for calculating water saturation. From the low resistivity reservoir characteristics, it is hoped that it can be used to find and identify the distribution of similar reservoirs so that potential reservoirs that have been overlooked so far can be identified, discovered and developed which in the end are expected to provide additional contributions both in terms of education and for the oil and gas industry in finding new reserves for oil and gas production in Indonesia.

Developing a Saturation Height Function (SHF) for the classified Hydraulic Flow Unit, a case study from the Middle Miocene Carbonate reservoir in the Song Hong Basin, Vietnam

The Middle Miocene carbonate is the main gas bearing reservoir in the southern part of the Song Hong basin. Carbonate reservoirs present challenges for engineers and geologists to characterize because of their complexity due to depositional and diagenetic processes. The log based calculated water saturation Sw for this complex carbonate reservoir bears a lot of uncertainties due to its heterogeneity that strongly affects to m and n values in the water saturation equation. As the calculated Sw strongly affects the Hydrocarbon Initially In Place (HIIP) estimation, the production performance prediction, etc., the alternative method using the Saturation Height Function (SHF) based on the capillary pressure was introduced. The SHF building process uses the combination of the result of HFU classification and permeability prediction given by machine learning methods, which have become a very useful tool for complex reservoir characterization such as carbonate reservoir for decades. Our obtained results allowed us to subdivide the carbonate reservoir into 5 Hydraulic Flow Unit (HFU) by using unsupervised machine learning methods, and then the capillary pressure (Pc) curves were classified and a proper saturation-height function using Skelt Harrison equation was assigned to individual HFU after testing with common SHF equations. The HFU number for each individual point was predicted based on the log data measured along the borehole using the supervised machine learning techniques while the Sw was calculated using the chosen SHF model and the FWL defined on log curves and pressure data from RCI measurement. The Sw computed from the SHF model is reliable and can be used for water saturation estimation for the whole field for known FWL and predicted HFU numbers.

Electrical Resistivity Tomography (ERT) Monitoring of Groundwater Flow Condition Surround Deep Excavation in Slope Area

Indonesia is a country with lots of mountain and hills, conducting deep excavation around slope areas will affect the groundwater. Deep excavation will change the groundwater table and hydrogeological condition. In order to monitoring the groundwater flow from the deep excavation in slope area, Electrical Resistivity Tomography used to monitoring the area surround the deep excavation project. The resistivity distribution in the area can be determined by knowing the condition of the soil around the deep excavation project through ERT (Electrical Resistivity Tomography). By utilizing passive ERT, vital parameters, such as porosity and volumetric water content, can be estimated, enabling the calculation of water saturation maps from inverted resistivity values. The resistivity conditions of the soil are continuously monitored, leading to the identification of patterns in soil resistivity movement, and the detection of potential instabilities at the boundaries between areas with varying water saturation levels.

New Iterative Resistivity Modeling Workflow Reduces Uncertainty in the Assessment of Water Saturation in Deeply Invaded Reservoirs

A new iterative modeling workflow has been designed to reduce the uncertainty of water saturation (Sw) calculations in the tight Barik sandstone in the Sultanate of Oman. Results from this case study indicate that Sw can be overestimated by up to 20 s.u. if the as-acquired deep resistivity is used in volumetric calculations. Overbalanced drilling causes deep invasion of water-based mud (WBM) filtrate into porous and permeable rocks, leading to the radial displacement of in-situ saturating fluids away from the wellbore. In low-porosity reservoirs drilled with WBM, the inability of the filtration process to quickly build impermeable mudcake translates into long radial transition zones. Under certain reservoir and drilling conditions, deep resistivity logs cannot reliably measure true formation resistivity and are, therefore, unable to provide an accurate assessment of hydrocarbon saturation. The effect of mud-filtrate invasion on resistivity logs has been extensively documented. Processing techniques use resistivity inversion and tool-specific forward modeling to provide uninvaded formation resistivity logs, which are much better suited for in-place resource volume assessment. However, sensitivity analysis shows that the accuracy of invasion-corrected logs dramatically decreases as the depth of invasion increases, whereby the inversion process needs to be further constrained. The new workflow is designed to reduce the non-uniqueness of true formation resistivity models so that they honor multiple and independent petrophysical data. The inversion routine utilizes a Bayesian algorithm coupled with Markov-Chain Monte Carlo (MCMC) sampling. Inversion results are iteratively modified based on two rock property models : one derived from rock-core data (helium expansion porosity and Dean-Stark saturations) and the other using an equivalent log interpretation of thick reservoir intervals from oil-based mud (OBM) wells. Simulated borehole resistivity is compared to field logs after each validation loop against rock property models. The new inversion-based workflow is extensively tested in the unconventional tight Barik Formation across water-free hydrocarbon and perched water intervals, and inversion-derived Sw models are independently validated by capillary-pressure-derived saturation-height models and fluid inflow rate from production logs.

The Comparation of Water Saturation Approaches to Reveal a Low Resistivity Reservoir Potential Case in Gumai Formation, South Sumatra Basin

The LRLC reservoir zone has been identified in SN-3 well, at the DAP-1 interval on Gumai Formation. This interval has a low resistivity value from 3-5 ohm.m and the drill stem test (DST) results show oil with gas without water. This study is objected to identify the causes of LRLC reservoir in gumai formation and finding a suitable sw calculation method. Some data such as well logs, reports, cores, and XRD are used to calculate petrophysical parameters such as Vsh, Phie, and Sw, and would be validated by DST data. Water saturation (Sw) calculations from Archie and the CEC method (Waxman Smits, Dual Water, Juhasz) were performed and the results were compared. The results showed that the main cause of the DAP-1 interval LRLC zone was the presence of clay minerals consisting of mixed layers (Illite/smectite). These clay minerals will be associated with high cation exchange capacity (CEC) values, with the value 70 (meq/100g), which can increase conductivity and reduce resistivity values. Based on lumping the more optimistic results of sw calculation from Waxman Smits Sw method (Sw based on CEC method). The DST data on the SN-3 well does not have water test data, so the calculation of the Sw value that is close to the Swirr value is considered the most suitable Sw for the low resistivity reservoir conditions of the Gumai Formation in the study area. The best practice for low resistivity reservoir for suitable petrophysical calculation is necessary to pay attention to the rock lithology conditions, the presence of mineral clay, and determining suitable Sw appropriate to the reservoir conditions

Numerical Simulation of Electrical Properties of Carbonate Reservoirs Using Digital Rocks

Rock electrical experiments are essential means of researching the conductive properties of rocks and are fundamental to interpreting resistivity logging. Carbonate rocks have more complex pore structures than sandstone, which results in more complex conductive properties. However, conducting experiments on representative rock samples from carbonate reservoirs is difficult, making it challenging to study the micro factors affecting electrical properties. Therefore, researching the conductive properties of carbonate rocks is difficult. To address this, in this paper, three-dimensional (3D) digital rock models with different porosities are generated, and conductive simulations are carried out on these models using the finite element method (FEM). Firstly, a micro-computed tomography (μ-CT) 3D image of a carbonate rock is obtained. Secondly, mathematical morphology-based methods are used on the μ-CT image to generate cores with varying porosities and fluid distributions. Then, the electrical properties are simulated using the FEM method, and the results are analyzed. The results reveal that the formation factor of the reservoir is mainly influenced by the shape and structure of the pores. The Archie equation is more suitable for carbonate reservoirs with water saturation levels greater than 60%. The wettability of the rock can alter the distribution of fluid in the reservoir space under different water saturation conditions. In pure water-wet rocks, the water phase mainly occupies small pores, while the oil phase occupies larger pores. As a result, compared to pure oil-wet rocks, water-wet rocks have more conductive channels and better conductivity. Therefore, it is important to determine the wettability of the rock when calculating water saturation using the Archie equation. The saturation index value of water-wet carbonate rock is about 2, while that of oil-wet rock is around 3–4. This research lays a foundation for studying the electrical conductivity of carbonate reservoirs using digital rocks.

Cementation Exponent Estimates and their Impact on Oil Initially in Place Calculations : A Case Study from Lower Qamchuqa Formation in the Bai Hassan Oilfield, Northern Iraq

The effect of Cementation Exponent has been investigated on the calculated water and hydrocarbon saturations and the estimated oil initially in place. The Aptian Lower Qamchuqa (Shua'iba) Formation from the well BH-96, Bai Hassan Oilfield, is used as a case study. Four different values of Cementation Exponent were applied in calculating water saturation using the Archie equation. The static value of 2 commonly used for carbonate reservoirs was one of the cases used to calculate an average of 68% water saturation. In the second case, the value of 1.81 is used for m based on the average m values obtained through testing 22 core samples in the laboratory. The average of 60.5% Sw was calculated for the studied part of the Lower Qamchuqa Formation in this case. The Pickett plot method was used as a third case, and 1.65 was estimated as the m value through which the average of 54% Sw was calculated. In the fourth case, variable values of m were used for the different depth intervals from which core samples were tested in the laboratory. In this case, the calculated average water saturation was equal to 60%. The upper part of the studied section was generally of low porosity and high water saturation, whereas the middle and the lower parts contained hydrocarbons with different saturations. Using shale content, porosity, permeability, and water saturation cutoffs for the four different cases, the net to gross pay ratios were calculated. The ratios ranged between 56.5% in the case of using the 1.65 value of m and 47.6% in the case of using variable core tested m values, and accordingly, the difference of about 4 m in net thickness is expected to be underestimated or overestimated, respectively, when is calculated.

Electrochemical response of unconsolidated sediment during liquid pore water phase change and its inspiration for gas hydrate saturation calculation

Electrical exploration is one of the most effective methods to estimate the location and abundance of hydrates. To better understand the relationship between electrical resistivity and gas hydrate saturation, the conduction mechanism of hydrate-bearing sediment needs to be investigated. Ice-bearing sediments with physical properties similar to hydrate-bearing sediment were synthesized using NaCl solution with different initial salinity. Electrical resistivity was detected using electrochemical impedance spectroscopy. Meanwhile, the distribution of relaxation time (DRT) was utilized to separate the electrochemical processes with various time constants and to analysis the conductive mechanism in depth. It is found that ion migration is the main conductive path of ice-bearing sediments. Salt ion transport is the main conducting path when ice saturation is low. Conversely, hydrogen or salt ion transport through ice is the main conducting path when ice saturation is high. The dual effects of ice on ion transport can be reflected in reducing and enhancing pore water resistivity. Based on the DRT results, increased ice saturation in larger pores hiders salt ion migration and promotes hydrogen ion movement, while the ice-sediment interface in smaller pores may form a highly conductive path. Moreover, it is also found that the DRT can be used to calculate water saturation. A cubic polynomial relationship is suggested between the DRT curve area and ice saturation. The application of electrochemical method to data analysis is very beneficial and can provide valuable information for obtaining more reliable hydrate saturation from electrical resistivity data. The results of this study are helpful to improve gas hydrate saturation interpretation models and provide significant guidance for gas hydrate production strategy design.

Saturation Determination and Fluid Identification in Carbonate Rocks Based on Well Logging Data: A Middle Eastern Case Study

In the Middle East, there remain many technical challenges in the water saturation evaluation of carbonate rocks and the effective identification of reservoir fluid properties. The traditional Archie equation is not applicable to carbonate reservoirs with complex pore structures and varying reservoir space distribution, as there are obvious “non-Archie” phenomena. In this paper, by analyzing the experimental data on the rock resistivity of the target formation in the study area and analyzing the relationship between stratigraphic factors and porosity, the previous fitting method was modified as a result of using the actual data while avoiding the cementation index as a way to improve Archie’s formula to evaluate the water saturation. Based on the improved Archie formula, the mathematical differential operation of water saturation and porosity was carried out using the formation resistivity. The calculation results of irreducible water saturation were used to calibrate the oil layer, and the water layer was calibrated when the water saturation was 100%, allowing for a novel reservoir fluid property identification method. This total differential method can effectively identify the oil-down-to (ODT) and water-up-to (WUT) levels in an oil–water system and then accurately divide the transition zone of the oil–water layer. When this method was applied, the identification results were in good agreement with production conclusions and test data with an accuracy rate of 89.95%. Although the use of geophysical logging data from open-hole wells combined with the total differential method is only applicable to wells with similar logging time and production time, it is possible to compare geophysical logging data from different periods to construct oil–water profiles to observe the changes in ODT over time to guide development and adjust production plans. The proposed reservoir fluid property identification method and the improved water saturation calculation formula can meet the requirements of water saturation evaluation in the target block with low calculation cost and easy implementation, which provides a new method for water saturation evaluation and rapid identification of reservoir fluid properties.

Assessment of the petrophysical properties and hydrocarbon potential of the Lower Miocene Nukhul Formation in the Abu Rudeis-Sidri Field, Gulf of Suez Basin, Egypt

Lower Miocene rift sediments of the Nukhul Formation are one of the prominent hydrocarbon producers in the Gulf of Suez basin. In this study, we focused on the oil producing Nukhul sandstones of the Abu Rudeis-Sidri Field, located in the east central Gulf of Suez. Nukhul Formation is characterized by the prominent low amplitude seismic reflectors and represents the youngest identified reflector (Lower Miocene) in the study area. Petrophysical assessment was carried out using wireline logs to infer the reservoir characteristics. The Nukhul sandstone reservoir exhibits lower shale volume (< 0.1 dec dominantly), 0.07–0.16 dec total porosity and effective porosity up to 0.13 dec within the Nukhul sandstone interval. Bulk density-neutron porosity cross plot infers primarily sandstone matrix with the influence of carbonates, which characterizes the studied reservoir as calcareous sandstones. Spectral gamma ray data indicates montmorillonite as the principal clay phase along with minor kaolinite and illite. The calculated water saturation of the reservoir zone in the three productive wells ranges between 0.17 and 0.34 dec (i.e., the hydrocarbon saturation equals 0.66–0.87 dec). A small range of bulk volume of water (0.011–0.03 dec) indicates superior quality of the hydrocarbon-bearing sandstone intervals. Absence of productive sands in one of the studied wells, drilled in the hanging wall implied structural control on hydrocarbon accumulation in the study area. This study provides crucial insights regarding the quantitative petrophysical characteristics, reservoir quality distribution and hydrocarbon potential of the Lower Miocene Nukhul clastic reservoir.

Estimating electrical resistivity from logging data for oil wells using machine learning

Formation resistivity is crucial for calculating water saturation, which, in turn, is used to estimate the stock-tank oil initially in place. However, obtaining a complete resistivity log can be challenging due to high costs, equipment failure, or data loss. To overcome this issue, this study introduces novel machine learning models that can be used to predict the electrical resistivity of oil wells, using conventional well logs. The analysis utilized gamma-ray (GR), delta time compressional logs (DTC), sonic shear log (DSTM), neutron porosity, and bulk density. The study utilized a dataset of 3529 logging data points from horizontal oil carbonate wells which were used to develop different machine learning models using random forest (RF) and decision tree (DT) algorithms. The obtained results showed that both models can predict electrical resistivity with high accuracy, over 0.94 for training and testing data. Comparing the models based on accuracy and consistency revealed that the RF model had a slight advantage over the DT model. Based on the data analysis, it was found that the formation resistivity is more significantly impacted by GR logs compared to DTC logs. These new ML models offer a low-cost and practical alternative to estimate well resistivity in oil wells, providing valuable information for geophysical and geological interpretation.

Estimation of Petrophysical Properties for Zubair Reservoir in Abu-Amood Oil Field

The petrophysical analysis is significant to determine the parameters controlling the production wells and the reservoir quality. In this study, Using Interactive petrophysics software to analyze the petrophysical parameters of five wells penetrated the Zubair reservoir in the Abu-Amood field to evaluate a reservoir and search for hydrocarbon zones. The available logs data such as density, sonic, gamma ray, SP, neutron, and resistivity logs for wells AAm-1, AAm-2, AAm-3, AAm-4, and AAm-5 were used to determine the reservoir properties in Zubair reservoir. The density-neutron and neutron-sonic cross plots, which appear as lines with porosity scale ticks, are used to distinguish between the three main lithologies of sandstone, limestone, and dolomite. The corrected gamma ray log was used in all wells to determine the shale volume. Neutron-sonic log was used to calculate porosity at the reservoir unit while sonic log was employed to estimate the porosity at poor hole conditions and (non- reservoir units). Furthermore, the Indonesia model was used to calculate water saturation in Zubair reservoir and compared with the Archie model. Finally, Flow zone indicator method was used for permeability evaluation. The results show that the Zubair reservoir is primarily consists of sandstone, shale with compacted limestone, which was improved by the cuttings description report. The core porosity was validated in AMM-1, AMM-2, AMM-3and AMM-5 wells. Indonesia equation provides an optimum estimation in shaly sand zones since the Archie model takes the matrix conductive and the fluid conductivity into account. Four hydraulic flow units produced from reservoir quality index against normalized porosity index cross plot and obtained Four equations from porosity- permeability relationship of core data. The general interpretation presented that the most of hydrocarbons are located in depth (3332.8 m to 3415.8 m) which represents the best layer in Zubair reservoir, the properties of this layer in well AMM-1better than in well AMM-2. Finally, the layer from 3574.8 m to3638.3 m supposed to be water layer rather than hydrocarbon layer.